2. INTRODUCTION
▪ Natural Language Processing is a subfield ofArtificial Intelligence
and linguistics devoted to making computers understand the
statements or words written by humans.
A language is a system, a set of rules or symbols.
1. Symbol is combined and used for conveying information or
broadcasting the information.
2. Rules of grammar are used for handling symbols.
3. INTRODUCTIO
N
▪ The history of NLP generally starts in the year the 1950s. In 1950,
Alan Turing published an article titled "Machine and Intelligence",
which advertised what is now called theTuring test as a subfield of
intelligence.
▪ Natural Languages are languages that living creatures use for
communication
▪ Artificial Languages are mathematically defined classes of
signals that can be used for communication with machines
▪ A language is a set of sentences that may be used as signals
to convey semantic information
▪ The meaning of a sentence is the semantic information it conveys
4. PROBLEMS FACED IN NLP
1. Incomplete description
2. Same word different Meanings
3. NewWords, Expressions and Meanings are generated quite freely.
4. There are a lot of ways of telling the same thing.
5. STEPS OF NATURAL LANGUAGE PROCESSING
▪ MORPHOLOGICALANALYSIS: INDIVIDUAL WORDS ARE ANALYSED INTO THEIR
COMPONENTS, AND NON-WORD TOKENS SUCH AS PUNCTUATIONS ARE
SEPARATED FROM THE COMMENTS.
▪ SYNTACTICANALYSIS: LINEAR SEQUENCES OF WORDS ARE TRANSFORMED INTO
STRUCTURES THAT SHOW HOW THE TERMS RELATE.
▪ SEMANTICANALYSIS: THE STRUCTURES CREATED BY THE SYNTACTIC
ANALYSER ARE ASSIGNED MEANINGS.
▪ DISCOURSE INTEGRATION: THE MEANING OF AN INDIVIDUAL SENTENCE MAY
DEPEND ON THE SENTENCES THAT PRECEDE IT AND MAY INFLUENCE THE
PURPOSES OF THE SENTENCES THAT FOLLOW IT.
▪ PRAGMATICANALYSIS:THE STRUCTURE REPRESENTING WHAT WAS SAID IS
REINTERPRETED TO DETERMINE WHAT WAS MEANT.
6. SYNTAX ANALYSIS
▪ The lexicon of a language is its vocabulary which includes its words
and expressions. Morphology depicts analysing, identifying and
description of the structure of words.
▪ It involves dividing a text into paragraphs, words and the sentences
▪ The words are generally accepted as being the minor units of
syntax.The syntax refers to the rules and principles that govern the
sentence structure of any individual languages
7. SYNTACTIC ANALYSIS
• Syntactic analysis or parsing may be defined as the
process of analyzing the strings of symbols in natural
language conforming to the rules of formal grammar.
• The origin of the word ‘parsing’ is from the Latin word
‘pars’ which means ‘part’.
8. CONCEPT OF PARSING
The primary roles of the parse include−
•To report any syntaxerror.
•To recover from commonly occurring errors so that the processing of the remainder of
the program can be continued.
•To create a parse tree.
•To create a symbol table.
•To produce intermediate representations (IR).
9. SYNTACTIC ANALYSIS
EXAMPLE
▪ A parse tree :
John ate the apple.
1. S -> NPVP
2. VP ->V NP
3. NP -> NAME
4. NP ->ART N
5. NAME ->John
6. V -> ate
7. ART-> the
8. N -> apple
S
NP VP
NAME
John
V
ate
NP
ART N
the apple
10. SEMANTIC ANALYSIS
▪ It must map individual words into appropriate objects in the
knowledgebase or database.
▪ It must create the correct structure corresponding to how the
individual words’ meanings are combined.
▪ Thus, a mapping is made between the syntactic structures and
objects in the task domain.The forms for which no such mapping is
possible are rejected.
▪ Eg: the sentence "Colorless green ideas…" would be rejected as
semantically anomalous because colourless and green make no
sense.
11. CONCLUSION
• Human level or Human readable natural language
processing is an AI-Complete problem.
• It is equivalent to solving the central artificial intelligence
problems and making computers as intelligent as people
• Make computers so they can solve problems like humans,
think like humans, perform activities that humans can’t
perform, and make them more efficient than humans.
• NLP future is closely linked with the growth of Artificial
Intelligence
• As natural language understanding or readability improves,
computers, machines, or devices can learn from the
information online and apply what they learned in the real
world.
